Mobility empowered biometric appliance a tool for real-time verification of identity through fingerprints

ABSTRACT

A system for transforming an image of a fingerprint, comprises a mobile device, comprising: a first communication interface, a camera configured to capture at least one image of at least one fingerprint, and a mobile device processor configured to execute instructions, the instructions configured to cause the mobile device processor to receive the image from the camera and transmit them to an image processing system via the first communication interface; and an image processing system, comprising: a second communication interface configured to receive the image, and an image processor configured to execute instructions, the instructions configured to cause the image processor receive the image form the second communication interface, and: render the image into a high contrast image, establish focus and image resolution for the image, perform noise reduction on the image, and perform distortion elimination on the image.

BACKGROUND

1. Field

The present invention is directed generally to biometric recognition,and more particularly to identifying persons using fingerprints capturedthrough smartphones and other mobile appliances.

2. Background

Fingerprints are truly the “human barcode” and among the best measuresof human identity available. Fingerprints are similar to DNA asbiometric identifiers because they can be obtained either (1) directlyfrom individuals or (2) from things individuals have touched in placesthey have been. An additional advantage of fingerprints is they arereadily matched to each other through well proven techniques; however,“traditional” fingerprints represent only a portion of what the handoffers in terms of identity. Other data available take the form of“palmprints,” which comprise a class that includes not only the palm butalso includes the second and third joints of the fingers, and the fingersides and tips and the sides of the hand (“writer's palm”).

There are many portable, or embedded fingerprint scanners. Because thefocus of such conventional fingerprint capture technology is on fingershowever, the palm class of prints is often ignored. In fact, mostportable scanners have no palm scanning capability because such ascanner would require a large platen (window) to scan a full palm.

SUMMARY

Systems and methods for using a portable device such as a smartphone forreal-time biometric identification are described herein.

According to one aspect, A system for transforming an image of afingerprint, comprises a mobile device, comprising: a firstcommunication interface, a camera configured to capture at least oneimage of at least one fingerprint, and a mobile device processorconfigured to execute instructions, the instructions configured to causethe mobile device processor to receive the image from the camera andtransmit them to an image processing system via the first communicationinterface; and an image processing system, comprising: a secondcommunication interface configured to receive the image, and an imageprocessor configured to execute instructions, the instructionsconfigured to cause the image processor receive the image form thesecond communication interface, and: render the image into a highcontrast image, establish focus and image resolution for the image,perform noise reduction on the image, and perform distortion eliminationon the image.

BRIEF DESCRIPTION OF THE FIGURES

The above and other aspects and features of the present inventiveconcept will be more apparent by describing example embodiments withreference to the accompanying drawings, in which:

FIG. 1 illustrates a smartphone-based fingerprint capture processaccording to one example embodiment;

FIG. 2 illustrates an example use of the system and methods describedherein;

FIG. 3 illustrates a sample series of screens presented to the user thatcan comprise a user interface for use with the systems and methodsdescribed herein in accordance with one embodiment;

FIG. 4 illustrates the interaction between the mobile device configuredin accordance with FIGS. 1-3 and an AFIS where fingerprint matchingoccurs in accordance with one example embodiment;

FIG. 5 is a flow chart illustrating an example process of converting animage into a high contrast representation of ridge flow;

FIG. 6A is an example of a fingerprint specimen;

FIG. 6B is an example of a high-contrast representation of thefingerprint of FIG. 6a produced using the process of FIG. 5;

FIG. 7 is a flow chart illustrating an example process for automatedfocusing for fingerprints in accordance with one embodiment;

FIG. 8 illustrated a finger image being transformed into two images: oneshowing ridges and the other showing wrinkles according to the processof FIG. 9;

FIG. 9 illustrates an example wrinkle removal process according to oneembodiment;

FIG. 10 shows an original “unflattened” fingerprint image and a“flattened” fingerprint image produced according to a describedfingerprint flattening process.

FIG. 11 shows various “poses” that can be captured to create sufficientinformation to create a rolled-equivalent fingerprint in accordance withone embodiment;

FIG. 12 shows an example of ridge structure in composite image renderedfrom a burst of images using super-resolution techniques in accordancewith one embodiment;

FIG. 13 shows an overview of a “ridge-centric” matching process whenapplied to latent fingerprint matching in accordance with oneembodiment;

FIG. 14 illustrates the process of applying and Afterburner applied toimages captured and returned from an AFIS search in accordance with oneembodiment;

FIG. 15 illustrates an example process for converting, e.g., asmartphone image to a viable fingerprint according to one embodiment;

FIG. 16 shows a fingerprint image compared against a rolled (live scan)reference print for the same finger;

FIG. 17 shows the same process of FIG. 16 applied to a section ofpalmprint;

FIG. 18 shows a schematic of two potential operational scenarios forperforming AFIS queries using, e.g., smartphone devices in accordancewith one embodiment;

FIG. 19 shows a photograph of fingers and a driver's license in the sameframe that can be automatically processed to extract the fingerprintinformation as well as the biographic information from the driver'slicense in accordance with one embodiment;

FIG. 20 shows an example smartphone application in use;

FIG. 21 shows a set of commercial glasses configured to implement thesystems and methods described herein in accordance with one embodiment;

FIG. 22 shows a schematic of glasses configured to implement the systemsand methods described herein in accordance with one embodiment; and

FIG. 23 shows the abundance of data available through wrinkles on thehands that will be visible at distances where fingerprints cannot becaptured.

DETAILED DESCRIPTION

The embodiments described herein provide end-to-end capability through asmartphone (1) to capture images of fingers, palms and other parts ofthe hand; (2) to render these into a high contrast image showing thebifurcations and terminations of the underlying ridge structure; (3) toidentify minutiae at these critical junctures in the ridge flow; (4) toassemble the minutiae information as an “AFIS Query,” i.e., a formattedpacket of information that can be directly submitted to an AFIS; (5) toreceive results from the AFIS; (6) to disambiguate these results, ifnecessary; and (7) to display information regarding matching referenceprints on the mobile device.

Such an end-to-end smartphone fingerprint capture device should meet twobasic technical requirements: (1) capturing an accurate depiction of thefingerprint ridges and (2) rendering these images with geometricaccuracy matching that obtained by a contact scanner. The embodimentsdescribed herein present a solution that operates on a conventionalsmartphone with no custom modifications that will fulfill theserequirements.

Conventional fingerprint capture technology works typically throughcapacitance or optical scanners—both requiring physical contact betweenthe finger and the scanner. This invention broadens the role forfingerprints as a premier measure of identity in three ways: (1) toexpand the opportunity for fingerprint capture by enabling commoditydevices such as conventional smartphones with cameras to becomefingerprint sensors with no specialized hardware, (2) to create afingerprint capture capability that avoids physical contact betweenindividuals—particularly under hostile conditions or cases where skin isfragile and would be damaged by contact, and (3) to expand the types of“prints” that can be used for identification to include palms and otherareas of the hand. This product will address multiple unmet needs.

Smartphones are ubiquitous devices with very powerful sensorcapabilities. When used to photograph fingers and hands, the smartphonecamera has the ability to generate images of sufficient qualitypermitting extraction of features determining biometric identity.Smartphone cameras also do not have the limitation of a platen and havesufficient image resolution to capture images from the fingers as wellas the palms.

The embodiments described herein vector the contactless image capturecapabilities of the smartphone into a robust mobile solution thatprovides the means of directly capturing finger and palm images andsubmitting these images for matching in an Automated FingerprintIdentification System (“AFIS”). AFIS technology receives featureinformation from fingerprints in the form of “minutiae”—the bifurcationsand terminations in the ridges of the finger—that are used to indexreference prints in a database. After receiving the minutiaeinformation, e.g., in the form of a search query, the AFIS can return alist of responsive reference prints from a database of known prints.

FIG. 1 illustrates a smartphone-based fingerprint capture processaccording to one example embodiment. As can be seen, an individual canplace their hand 102 onto the screen 104 of a smartphone 106. Softwarerunning on smartphone 106 can then process the finger prints, palmprints or both. On the right are conventional images captured via ascanner. The figure illustrates the image captured through thesmartphone 106 should be comparable in its structure and usage to afingerprint captured through a scanner.

It should be noted that in certain embodiments, the user can simplyhover their hand over screen 104. Further, the image capture mechanismcan be a photo or series of photos. Alternatively, the software canconfigure the phone 106 such that a video capture of the relevant printsis captured as the user places their hand on the screen 104 or bringsinto range of the video camera. The software can then cause a videocapture to begin. The software can further analyze the video imagesuntil an image of sufficient quality and resolution is captured, whichcan cause the capture to end. Instructions for the user can be presentedaudibly, on the screen 104 or both, directing the placement of theuser's hand until such a sufficient image is obtained.

FIG. 2 illustrates an example use of the system and methods describedherein. First, in frame 202, a fingerprint is captured using a COTS(Commercial off-the-shelf) smartphone with onboard software, configuredin accordance with the systems and methods described herein, to assistimage acquisition and rendering of the hand into useable fingerprints.The subject's fingerprint may have also been, e.g., scanned usingconventional techniques and therefore may exists within an AFIS. Theoutput from the smartphone as well as the scanning process should be inan EBTS, EFTS, or similarly formatted file. The output of the captureshown in frame 202 can then be transmitted to the AFIS as part of a“search request,” which can be sent from the mobile device. Metadata canalso be captured at this time either by typing in the data orphotographing identity cards or similar documents.

As illustrated in frame 204, the AFIS can return a candidate list ofsubjects responsive to the query. Given potential quality issues withthe query images not all may hit the correct reference; however, if anyindividual query image hits the reference, a full set of “tenprints”will be returned creating an “Afterburner” opportunity. This isillustrated in frame 206. Thus, the systems and methods described hereinprovide an “AFIS Afterburner” capability to match all the fingerscaptured by the device, e.g., device 106 in FIG. 1, with the full set oftenprints returned by an AFIS.

As illustrated in frame 208, the AFIS (and Afterburner) results areavailable for review by an examiner, if the actual implementationrequires human review.

A critical aspect of the software running on the mobile device, e.g.,smartphone 106, is the user experience. FIG. 3 illustrates a sampleseries of screens presented to the user that can comprise a userinterface 300 called “Slapshot”. Clockwise from the upper left corner,the images are described as follows. Screen 302 shows the basic openingscreen of the Slapshot application. From this screen 302, the usermoves, e.g., by selecting “New Capture,” to Screen 304, which provides asingle source of all functionality. In this example, the options fromScreen 304 include: biographic information, automatic identity documentcapture, face capture, a left slap (4 fingers) and a right slap (4fingers). In certain embodiments, two thumbs in a single image plusother functionality desired by a specific user can also be madeavailable.

Once a selection is made, e.g., “right slap,” then the user transitionsto Screen 306 where they are prompted, requested, directed, etc., toplace their hand on/over the screen. In this example of FIG. 3, it canbe seen that a target viewer with finger “outlines” to assistpositioning fingers is presented to the user. As the fingers arepositioned, the application can automatically determine focus, asdescribed above, and capture an image when the focus has been optimized.

Screen 308 shows sample metadata that can be captured along with thebiometric information. This information is configurable and can also becaptured from barcodes or other mechanisms contained in certain identitydocuments such as drivers' licenses.

FIG. 4 illustrates the interaction between the mobile device configuredas described above and an AFIS 406 where the fingerprint matchingoccurs. On the left, the user interface 300 running on an associatedmobile device is shown. As noted above, the mobile device can beconfigured, with the aid of user interface 300, to capture biometricimage data such as finger and palm prints. The image data is thentransmitted to the AFIS 406 for analysis. The image data can be sent asraw or processed image data, or as part of an identity document thatincludes the image data as well as meta and possibly other data.

A user interface 404 is illustrated on the right. User interface 404 canbe the user interface for an application that interfaces with the AFIS406. Such an application can if necessary create an identity document,convert an identity document into electronic text, render hand andfinger images into fingerprints, remove noise and other artifacts formthe fingerprints, prepare and submit a query file to the AFIS 406. Oncethe AFIS runs the query and returns results, the application can helpmatch the fingerprints against a full set of tenprints as describedabove and return the results to the mobile device application.

It will be understood that the AFIS 406 comprises the servers,processors, software, applications, user interfaces, data storagedevices, etc., needed to perform the functions of an AFIS. Further, userinterface 404 and the associated application can be accessed via aterminal 408 that is part of the AFIS 406 or simply interfaced with theAFIS 406. The application can then reside on the AFIS 406 or separate,e.g., on terminal 408. Terminal 408 can be located with the AFIS 406 orremote therefrom.

The process of transforming an image or a photograph into a fingerprintimage suitable for AFIS submission involves several steps includingrendering the photograph into a high contrast image, establishing focusand image resolution, noise reduction and distortion elimination.Additionally, after the results have been returned from the AFIS 406,post-processing can improve recognition results. These steps arediscussed through the ensuing paragraphs.

First, with respect to image rendering it is important to note thatcontact scanners capture fingerprint ridges by separating skin incontact with the scanner (ridges) from skin not making physical contact(furrows). When images of fingers are captured photographically, thereis no physical contact that can be used to separate ridges from furrows.So, this distinction must be made using only information available inthe image, which consists of the color and luminance values of pixels.

Because of the high resolution afforded by, e.g., modern smartphonecameras, the image quality is quite good enabling the visual resolutionof ridges and furrows and the detection of “Level 3” features such aspores in ridges as well as the contour of the ridges.

Various techniques are employed to transform a photograph of a fingerinto a high contrast image suitable for searching in an AFIS. Thesemethods are discussed in the ensuring paragraphs.

The first step in the rendering process is the enhancement of thephotographic images to improve contrast between ridges and furrows. Thetechniques employed to improve contrast uses specular reflection oflight from a finger surface, which varies depending on the local angleof the skin relative to the light source and camera. Contrastenhancement using adaptive histogram equalization allows for clearseparation between ridges and valleys, and permits accurate fusion ofmultiple images taken from different angles. Once a dense map ofcorrespondences is created between two or more images, an accurate depthmap can be created, and used to create a 2d projection of the 3d fingersurface: this is a rolled-equivalent fingerprint image.

Once the images have been enhanced, they can be rendered into highcontrast images resembling “traditional” fingerprints obtained byoptical or contact scanning. The method for rendering ridges isdiscussed in FIG. 5 and presented in U.S. Patent Publication No.2013/0101186, entitled “Systems and Methods for Ridge-Based FingerprintAnalysis,” which is incorporated herein by reference as if set forth infull. A modification to this method can be made to address the issue ofvariation in ridge separation often encountered in fingers. Thismodification involves applying this technique multiple times andcompositing the results to create the best image rendering.

As shown in FIG. 5, the process of converting an image into a highcontrast representation of ridge flow begins in step 502, where theimage is read into memory from a user-specified file.

In step 504, a bandpass filter can be applied. This is a filter thateliminates all frequency components of the image, except those lyingwithin a specified range. This exploits the fact that ridges on humanfingers tend to have a spacing that does not vary a great deal from 0.5mm; the frequency range cutoff values allow for a variation either sideof this value by a fairly generous factor of two. Clearly, therefore, itis necessary to know the image resolution, in order to convert thisaverage ridge wavelength into a width expressed as a number of pixels.One effect of the bandpass filter is to eliminate the zero-frequencycomponent, or “DC component”; this makes the mean intensity value equalto zero over any extended area of the image, which is part of therequirement for a normalized representation.

In step 506, the orientation pattern of the ridges is analyzed. Thisstep retrieves a number of quantities, including the orientation of theridge pattern at each point. Another quantity determined in the exampleembodiment is coherence. Coherence is represented by a number between 0and 1, and is a measure of how well defined the orientation pattern isat each point. A value of 1 corresponds to the optimum situation, whereall the intensity variation in the image is in one direction, e.g.,perpendicular to the ridges, with no variation in the direction parallelto the ridges. A value of 0 indicates no preference for one directionover another, as would occur in a region of uniform brightness or withrandom image noise that was not directionally dependent.

The ridge orientation field, along with other relevant parameters suchas coherence, can be obtained by a method that will be referred to asPrincipal Component Analysis. This process identifies the direction ateach point in the image in which the intensity variation per unit isgreatest; in a ridge pattern this is typically perpendicular to theridges. Because the intensity gradient along any direction, which is themeasure of the variation, can be positive or negative, the square of theintensity gradient is used. In particular, at each point the directionis identified for which the squared intensity gradient, taken along thisdirection and averaged over the neighborhood of the point, is a maximum.Best results are obtained if the radius of the neighborhood is aboutequal to the average ridge wavelength; using a smaller neighborhoodresults in oversensitivity to image noise, while too large a radius“smoothes out” the orientation field too much, and may result ininaccurate placement of the cores and deltas.

The direction Θ of maximal squared intensity gradient is given by:

2Θ=arctan(P/D)

where

D=mean(g _(x) ² −g _(y2) ⁾

P=mean(2g _(x) g _(y))

and g_(x) and g_(y) are the image intensity gradients in the x and ydirections respectively.

There are two values of (180 degrees apart) that satisfy this equation.This reflects the fact that orientation is an ambiguous quantity. Forexample, a road on a map is designated as running east to west, buttraffic on that road may be traveling either east or west.

Two other quantities are extracted at this stage. These are:

R=√{square root over ((D ² +P ²))}

E=mean(g _(x) ² −g _(y) ²)

Both these quantities are always non-negative. The energy E is a measureof the total image variation without reference to direction, while R,the directional response, measures the total directionally dependentimage variation. R is zero when the pattern is completely isotropic,i.e., when the average amount of variation is the same no matter whichdirection one moves within the image; it is equal to E when all thevariation is along one direction, as for example in the case of a set ofperfectly parallel lines. The quantity C=R/E therefore always liesbetween 0 and 1, and can be used as a measure of the pattern'scoherence, or how well the orientation of the pattern is defined. Verylow coherence values occur in areas where the fingerprint is smudged orotherwise corrupted, as well as in most parts of the background; C istherefore one quantity that is useful in separating the print foregroundfrom the background.

The quantities obtained in this analysis stage are used in the laternoise removal stages, and they also provide important cues whenperforming segmentation.

Next, in step 508, segmentation is performed by applying a series ofsegmentation masks. A segmentation mask is an image consisting of a setof binary values for all points in the image. Points assigned a value of1 are denoted “foreground”; points assigned a value of zero are denoted“background”.

In an embodiment, three different segmentation masks are generated,based on three different quantities. A coherence segmentation isgenerated by assigning a value of 1 to all points where the quantity C,defined above, is greater than a threshold value. The threshold may beselected by experimentation to correspond to characteristics of theimage. A value of 0.3 is typical of a coherence value at which the ridgeorientation is readily discernible.

In a preferred embodiment, this mask is modified to fill in holesoccurring at a singularity in the flow pattern (a core or delta point).At these points, the coherence drops to a very low value. This is notbecause the ridges are poorly defined at this point, but because theorientation varies rapidly over a small region of space. This leaves“holes” in the mask at these points, the size of the hole being roughlyequal to the radius of the neighborhood used in taking the means ofgradient quantities to calculate D and P above. This operation isreferred to as morphological closure.

The coherence segmentation is normally effective in including all thefingerprint regions where the pattern can be interpreted with the humaneye, and it masks out most of the background. However there are certaintypes of background features that show high directional coherence, suchas handwritten annotations, ruled lines on the card or the grain of thesurface on which the print was made. Thus, in a preferred embodiment,the coherence mask is supplemented by additional masks.

In the example embodiment, a second mask based on directional responseis generated based on the quantity R defined above. This quantity is amagnitude rather than a dimensionless quantity such as coherence; itmeasures the amount in intensity per pixel displacement, by which theintensity varies in a directionally dependent manner. This maskeliminates background regions where the pattern is faint but highlylinear. Many materials such as paper or wood exhibit a grain structurethat is normally much fainter than the fingerprint ridges and may evennot be discernible to the eye in the original image. This grainstructure will result in high values for coherence, so that a coherencemask alone will erroneously include these areas as part of theforeground.

The directional response mask is generated by identifying all pointswhere the quantity R is above a certain threshold. The threshold isselected based on the properties of the image, as follows.

The assumption is made that the fingerprint will occupy, e.g., at least5% of the image. Therefore, the quantity R_(m) is found, such that only5% of the points in the image have a value R>R_(m). If the foregoingassumption is valid, then this means that R_(m) will be a valuerepresentative of points within the print foreground. Some foregroundpoints will have a greater value of R; most will show a smaller value.

The threshold value R_(T) is then set to 0.01*R_(m). This allows themask to include regions where R is significantly less than the95-percentile value; however, it successfully masks out regionsdescribed above, namely parts of the background where there is a linearpattern corresponding to a very faint grain.

In this example embodiment, a third mask is generated based on the ridgefrequency extracted from the pattern. First, a binary version of theenhanced image is generated by replacing all positive image values by 1(white) and all negative values by 0 (black). Since the enhanced imageis normalized, the values are symmetrical about zero, so the resultingbinary image contains roughly the same number of on and off bits. Then,the borders of the black and white regions are identified. These arepixels whose binary value differs from the binary value of one or moreof its neighbors. Next, since the direction of the ridge normal isalready known, the number of on/off transitions per unit distance normalto the ridges is examined for each part of the image. The ridgefrequency is half this value.

The mask is defined by selecting points for which the measured ridgefrequency and the theoretical average frequency differ by less than arelative factor of 0.4. That is, if f_(m) is the mean frequency, f willlie between f_(m)/1.4 and 1.4*f_(m).

The frequency mask shows holes similar to those in the coherencesegmentation mask, and for the same reason; the core and delta pointsare points at which the orientation is ill-defined, therefore thefrequency, measured along a particular direction, is also notwell-defined. These holes are filled in using the same procedure as inthe coherence mask.

The frequency-based segmentation filters out parts of the backgroundcontaining features such as ruled lines or handwritten notes. Suchpatterns are highly linear, but they are typically isolated lines ratherthan a series of parallel lines such as is found in the ridge pattern.

The three segmentation masks described above are preferably combinedinto one final segmentation mask by an intersection operation. That is,a point is marked as foreground in the final mask if and only if it is aforeground point in all three individual masks.

In step 510, the orientation field is smoothed. This can reduce, and mayeliminate, the effect of isolated linear features that are notassociated with the ridges, such as skin folds, or handwritten linesdrawn across the pattern.

An accurate prior determination of the orientation field is preferred,since knowing the orientation at each point allows the process to avoidsmoothing out the pattern in the cross-ridge direction, which mayeliminate ridge features of interest. For this reason, in certainembodiments the process seeks to remove as much noise as possible fromthe derived orientation pattern before proceeding to the second stage.

The orientation, which is an angular measurement, can be smoothed by amethod of averaging angles, for example: Express the angle as a vectorquantity, e.g. a vector V with components Vx=cos(Θ) and Vy=sin(Θ). Vxand Vy are smoothed by taking a weighted mean over the image or aneighborhood within the image. The quantity is converted back to anangle by taking the angle defined by the smoothed components mean (Vx)and mean (Vy).

The example embodiment deals with two complications arising in the caseof ridge orientation patterns. The first is that orientation is anambiguous quantity, as noted above. An orientation of 30° isindistinguishable from an orientation of 150°. example embodimentcompensates for this factor by doubling the angle, then smoothing thedoubled angle (which we denote Φ) by means of a weighted averaging, andfinally halving the result.

The second complication is the core and delta points in the fingerprint.These represent singularities in the orientation field, and it is notpractical or desirable to directly apply smoothing at these points. Asimple smoothing generally has the effect of shifting the core or deltapoint to the wrong place.

A core point is characterized by the fact that, if a closed path istraced around the point and follow the behaviour of the orientation,this vector rotates by 180 degrees for a single clockwise traversal ofthe closed path. The doubled angle therefore rotates by 360 degrees. Thesame behavior happens at a delta point, except that the rotation is inthe opposite sense. In other words, the cores and deltas can be treatedas generating spirals in the orientation field, the spiral flows beingsuperimposed on an otherwise continuous flow pattern. The doubled angleΦ over the image can be expressed as:

Φ=Φ_(C)+Φ_(D)

where Φ_(C) is the residual field, and is Φ_(S) the spiral orientationfield resulting from the presence of the cores and deltas.

At any point (x,y) in the image, the spiral field from a core point P isthe bearing angle from the point (x,y) to the point P. This has thedesired property that when any closed path is traced around P, the angledoes one complete rotation.

Similarly the spiral field around a delta point is taken as the negativeof the bearing angle. This gives the required rotation of the vector, inthe opposite direction to the direction of the path traversal.

Core and delta points in the original Φ field are located in the exampleembodiment using a quantity called the Poincaré index. This is obtainedusing the spatial derivatives of the angle (in mathematical language, itis the curl of the x and y spatial derivatives of the angle), and itsvalue is 2π at a core point, −2π at a delta, and zero every where else.

In summary, in an example embodiment the orientation quantity issmoothed using the following steps:

-   -   1. Calculate the doubled angle Φ;    -   2. Locate the core points using the Poincaré index;    -   3. Calculate the spiral field across the image for each core and        delta point, and sum these to give Φ_(S);    -   4. Subtract Φ_(S) from Φ to give Φ_(C);    -   5. Smooth Φ_(C) using a weighted neighborhood average;    -   6. Add Φ_(S) back to the result to give a final smoothed field Φ        with the core and delta points preserved.

In step 512, ridge enhancement is performed. The ridge enhancementprocess is an image smoothing operation that smoothens intensityvariations in the direction parallel to the ridges, while those in theperpendicular direction are largely unaffected. The example embodimentseeks to avoid smoothing in the cross-ridge direction, since this wouldeventually destroy the pattern of ridges and valleys, which are featuresof interest to be enhanced rather than diminished.

Ridge enhancement is a process for reducing or eliminatingirregularities in the ridge pattern, making it conform more closely to atheoretically ideal ridge pattern. Ideally the pattern resembles a wavepattern with no breaks in the waves, with the crests and trough havingthe same amplitude everywhere. In this idealized ridge pattern theintensity is constant when one traces a path in the image parallel tothe ridges.

In the example embodiment, noise consisting of small intensityfluctuations in an image is reduced or eliminated by applying a suitablychosen smoothing filter, which replaces the intensity value at a pixelby a value calculated as a weighted average of pixels in a restrictedneighborhood. A modified process is desirable to ensure that anysmoothing takes place only in the direction parallel to the ridges,otherwise spatial averaging may reduce or eliminate the ridgesthemselves.

In an embodiment, a method described as oriented diffusion is employed.

This exploits the fact that, if an intensity profile is taken along thedirection of the ridge orientation, the humps and dips in the profileare related to the second spatial derivative of the intensity takenalong that direction. This can be seen by considering the intensity I asa function f of spatial location x, and expressing f as a Taylorexpansion centered on a reference value x₀:

f(x ₀ +d)=f(x ₀)+f′(x ₀)d+[f″(x ₀)]d ²/2+ . . .

where f′(x₀), f″(x₀) etc are the 1^(st), 2nd etc. derivatives off at thepoint x₀.

If we now take a small interval centered on x₀, for example allow d torange from −r to +r for some r, and examine the mean of the aboveexpression over the interval, we see that the term in f′ vanishesbecause the mean value of d is zero. An approximation to the mean valuecan therefore be made by taking:

mean(f)≅f(x ₀)+[f″(x ₀)]*mean(d ²)/2

Where the term “mean (d²)” is constant, and simply depends on the sizeof our chosen interval. The equality is only approximate, because thefull Taylor expansion contains higher order terms.

In the example embodiment, oriented diffusion is performed as follows:(1) Obtain the second spatial derivative f′ of intensity, taken at eachpixel in the direction of the ridge orientation; (2) Average thisquantity over a very small neighborhood of the pixel, where the size ofthe neighborhood used is somewhat less than the average ridgewavelength; (3) Apply the above formula to estimate the mean intensity;and (4) Repeat the above steps as often as desired.

Experimentation has shown that improvement in the ridge definition israpid for the first few iterations of the diffusion process, buteventually a plateau is reached at which further application of theprocess results in little noticeable improvement. This happens aftersomewhere between 50 and 100 iterations, depending on the quality of theinitial image. The number of iterations may be set by experimentationbased on typical input image quality.

In step 514, a quadrature operation is applied to the image, allowingthe intensity at each point to be expressed in terms of the amplitudeand phase of a sine wave. The quadrature operation follows the processesdisclosed by Larkin and Fletcher for obtaining the quadrature of atwo-dimensional image function. The original function, together with itsquadrature, can be combined to produce a complex valued functionrepresenting a periodic wave, and the phase at any point can be obtainedby examining the relative values of the real and imaginary parts of thecomplex function.

Obtaining the quadrature requires specifying the direction of the wavenormal at each point. This is at right angles to the ridges, but asnoted above, it is only possible to specify the ridge orientation asbeing in one of two directions, 180 degrees apart. This ambiguity in thewave direction results in a corresponding ambiguity in the phase;however, the quantity of primary interest in the height of the wave ateach point, measured by the cosine of the phase. The same cosine valueis found irrespective of which of the two possible directions was takenas the wave normal.

The end result, then, is a map showing the cosine of the phase. In sucha representation the wave crests all have the same intensity value (+1)and the troughs all have the same value (−1). Prints are normally takenusing a dark marking medium on a lighter background and sometimes thisrelationship switches when the prints come from photographs; thereforethe wave crests correspond to the inter-ridge valley axes and thetroughs correspond to the ridge axes. The amplitude is discarded; thecosine of the phase is the normalized image, since the cosine values liebetween −1 and +1.

Finally, in step 516, if applicable, the resultant smoothed andnormalized image and the foreground mask image are each written touser-specified storage locations. To illustrate the results of theexample process of FIG. 5, FIG. 6A is an example of a fingerprintspecimen and FIG. 6B is an example of a high-contrast representation ofthe fingerprint of FIG. 6A. In this particular high contrastrepresentation, ridges are shown in black and furrows as white.

The photograph-to-ridge rendering process can be applied iteratively toaccount for conversion of ridges of various widths. Based in itsintrinsic quality metric, the results can be composited to create asingle image that shows the optimal rendering of ridges.

Next, focus and image resolution work hand-in-hand to achieve a sharplyfocused image with an established resolution. Modern, e.g., smartphonesprovide control access to the onboard camera to set focus distancethrough software. A device configured as described herein achieves focusand resolution control by capturing a series of images at differentdistances, evaluating each photograph and selecting the one that is inbest focus. The best focus image, based on the given on board mobilecamera specifications, is taken from a continuous stream of fullresolution camera frames across a (small) configurable number of secondsand focus distances. The camera begins from its starting position andmoves incrementally to achieve the image in best focus guided byreal-time feedback on focus quality.

The focus in each frame can be determined by taking the average perpixel convolution value of a Laplace filter over a small region of thefull resolution image that the target's skin encompasses. The size ofthis region is adjusted based off of the current focal distance reportedby the camera to reduce the chance that background is included in targetregion, thus negatively impacting the averaged value. For larger focaldistances, the viewed target is smaller in pixel measurements, so theregion's size is reduced to better guarantee skin coverage within theentire region. Likewise, smaller focus distances have larger targetregions.

After each frame's focus value is calculated, the camera's focusdistance is adjusted in attempt to better the focus value upon the nextframe's capture. The determination of which direction (closer orfarther) to adjust the focus is based on the difference of the focusvalues of the last two frames in the following manner: 1) if the focusis getting worse, then reverse the direction of focus distanceadjustment, 2) if the focus is getting better, maintain the direction offocus distance adjustment. Initially the incremental step that the focusdistance is adjusted is large (and is configurable), but after eachfocus distance adjustment, the magnitude of the incremental step isslightly reduced. The adjustment of the incremental step continues untilthe incremental step is reduced to a configurable minimum value. Sincethe “ideal” focus distance is constantly changing due to both theunsteady camera and the unsteady target, this method good for quicklyadjusting the focus distance to the ballpark of where it should be tohave the target in focus, and then minimally adjusted for the remainderof the stream to capture a frame of the moving target at a locallymaximized focus value.

The steps involved in automated focusing for fingerprints is presentedin FIG. 7. First, in step 702, an image is captured at an initial focusdistance. Then in step 704, the captured image is convolved withLaplacian of Gaussian kernel. In step 706, scores are assigned to afiltered image reflecting the amount to fine edge resolution. In step708, the focus is then dynamically updated until an optimal distance isfound.

Once focus distance is established, it becomes the basis for calculatingimage resolution. The resolution of the best, full resolution image isderived from the focus distance, F_(D), recorded at the time the imagewas taken. The resolution of the image is equal to (W*F_(L))/(S_(x)*F_(D)) where W is the width of the camera image, F_(L) isthe focus length of the camera and S_(x) is the physical sensor size,e.g., the width in this case, of the camera. In the absence of theability to control focus distance, the conventional solution has been toplace an object of known dimension in the image. Such “target” basedtechniques can be used with older equipment where camera controls arenot provided. Computing image resolution using a target is a wellunderstood process and presented herein by reference.

The next image enhancement process involves the detection andelimination of noise artifacts that appear in photographic images. Amongthese artifacts are wrinkles and other creases that are present in thefinger but are reduced during compression during capacitance or opticalcontact scanning.

Images of fingertips exhibiting dermal ridge patterns often includecreases and wrinkles. These are peak or valley features whose widthranges from smaller than that of a dermal ridge to several times that ofa dermal ridge. The presence of wrinkles in a fingerprint image caninterfere with identification, by spoofing ridge endings orbifurcations, misleading orientation field estimation based ondirectional energy, or by causing some regions of the print to faillocal quality tests. This can result in otherwise informative regions ofthe dermal ridge pattern being heavily discounted or removed fromconsideration.

Very large or small wrinkles can be effectively eliminated bybandpassing, but wrinkles whose width is within a factor of 3 of theridge width require more sophisticated treatment.

This is a problem of source separation. In processing images forsubmission to a biometric identification system, the goal is to retainintensity variations caused by dermal ridges and discard intensityvariations caused by other sources. In the spatial domain these patternsoverlap, but in the frequency domain they are separable to the extentthat they differ in either frequency or orientation.

The orientation field of the dermal ridges is smooth and continuousacross the fingertip except at cores and deltas. Furthermore, someregions of the fingertip tend to follow a particular orientation: ridgesnear the bottom of the fingertip, just above the distal flexion crease,have an orientation very close to horizontal. Ridges near the top of thefingertip are generally arch-shaped. By contrast, the area in the middleof the fingertip does not have a predictable orientation.

These characteristics of dermal ridge patterns are not shared by wrinklepatterns, which enables us to mask them out by the following process:(1) copy the region of interest to a buffer; (2) apply a windowingfunction to the image, such as the Hamming, Hann, or Gaussian window;(3) apply a 2D Fourier transform to the windowed image; (4) rotate eachquadrant of the FFT image 180 degrees in order to relocate the DCcomponent and low frequencies from the corners to the image center. Inthe representation, the orientation of each frequency component isdetermined by its bearing relative to the image center; (5) for eachcomponent on the image whose orientation exceeds a threshold angle fromthe predicted orientation (15 degrees, for instance), set its value tozero. The DC component should remain unchanged; (6) rotate the quadrants180 degrees back to their original positions, and apply the inverseFourier transform; (7) replace the region of the original image with aweighted sum of the original image and the inverse FFT image, where theweight of each pixel of the reverse FFT image is proportional to thewindowing function applied prior to the FFT.

This procedure can be repeated across all regions of the image where theorientation field can be accurately estimated. FIG. 8 shows a fingerimage 802 transformed into two images: one image 804 showing ridges andthe other image 806 showing wrinkles. FIG. 9 presents a schematic of thewrinkle removal process as herein described. An original finger imagewith wrinkles is shown from the left in the bottom row of images in FIG.9, and a region of interest of the image where the wrinkle removalprocess is to be applied is shown within a rectangular bounding box. Thefirst image from the left in the top row of images shows the result of a2D FFT of the windowed subimage of the region of interest in FIG. 9. Thesecond image from the left in the top row of images shows the result ofrotating the FFT image to relocate the DC component and low frequenciesfrom the corners to the image center. The rotated FFT image is thenseparated into two rotated FFT subimages: the lower rotated FFT subimageis generated by setting those frequency components having orientationsexceed a threshold angle from the predicted orientation to zero, and theupper rotated FFT subimage is generated by setting those frequencycomponents having orientations below the threshold angle from thepredicted orientation to zero. Hence, the upper rotated FFT subimagecontains mainly the wrinkle information whereas the lower rotated FFTsubimage contains mainly the dermal ridges information, therebyseparating the wrinkles from the dermal ridges. As can be seen in bothsubimages, the DC component at the center remains unchanged. The two FFTsubimages to the right of the two rotated FFT subimages are obtained byrotating the two rotated FFT subimages back to their original positions,and the lower FFT subimage containing horizontal patterns is thewrinkle-removed FFT image of the region of interest in the originalimage. Finally, an inverse FFT is applied to the lower FFT subimage, andthe result of which is used to generate a replacement image to replacethe region of interest in the original image. In some embodiments, thereplacement image is the weight sum of this inverse FFT subimage and theoriginal region of interest. Hence, the second image in the bottom rowof images shows the original image with a wrinkle-removed region ofinterest.

While dermal ridges can be considered permanent biometric identifiers,the presence, location, and orientation of skin creases can be used toestablish identity as well.

The time span over which different kinds of creases remain useful rangesfrom temporary wrinkles lasting for minutes or hours, to deep creasessuch as the flexion creases of the digits and major creases of the palm,attributes of which can last for a lifetime. Certain arrangements ofpalmar creases, e.g. the ‘simian crease’, are used as diagnostic signsfor genetic abnormalities.

As a biometric, digital flexion creases and palmar creases have theadvantage of being long-term features that are substantially larger thandermal ridges, allowing them to be detected from a greater distance orwith a lower-resolution image sensor.

Some of the characteristics that can be used to compare a pair ofcaptured crease images are: (1) the locations of points of intersectionbetween palm creases, which could be compared using an iterative closestpoint procedure to determine the best fit projective transformationbetween the two sets of points, and calculating the error according tothe distances between point pairs; (2) a collection of distancesmeasured between a parallel pair of creases, within a landmark-definedregion; (3) the count and distribution of individual wrinkles that makeup a flexion crease at each joint of the digits could be compared, usinglocal binary patterns (LBP) or another image-based feature set tomeasure the similarity between brightness and contrast-normalized imagesof the skin of the creases.

FIG. 23 shows the abundance of data available through wrinkles on thehands that will be visible at distances where fingerprints cannot becaptured. The RSM matching method described below can be used to matchthese wrinkle features similar to the way it matches the friction ridgesof the finger and palm.

The next image enhancement process involves image flattening. Fingersrepresent 3-dimensional objects that are captured differently by acamera lens than they are captured by contact scanning. The systems andmethods described herein can achieve flattening capability in two ways:The first flattening method develops a 3-dimensional approximation ofthe finger surface as a vaulted shape. Once this shaping has been fittedto a specific fingerprint, the fingerprint can be transformed to itsflat equivalent. The second flattening method entails a series of stepsas follows: (1) Run horizontal lines across the image or superimpose agrid on the image, (2) Segment the lines whenever they cross a ridgecenterline; (3) Sort the segments by length; (4) Find all the segmentsbelow a certain length; (5) find the median dimension for theridge-to-ridge distance; triangulate all the points in the image; andtransform the triangulation stretching all the “short” segments to themedian dimension.

FIG. 10 shows an original “unflattened” fingerprint image 1002 and“flattened” fingerprint image 1004 produced according to the aboveprocess(es).

The “slap” or “rolled-equivalent” images obtained by the MEBA areintended to conform to the NIST draft standard for Fast Ten-PrintCapture (FTC) devices, with specific requirements for gray-levelcontrast and geometric accuracy. These standards mirror earlierrequirements used to ensure that live-scan equipment can be widelyaccepted as a substitute for scanned fingerprint cards.

Prints captured with a smartphone can either be the equivalent of“flats” (or slaps) or “rolls”. Flat prints just show the visible surfaceof the print whereas rolls show the visible area as well as the sidesand part of the tip. To create flats, a single image will suffice,however, rolls require multiple photographs that are ultimately woventogether. FIG. 11 shows various “poses” that can be captured to createsufficient information to create a rolled-equivalent fingerprint from a,e.g., smartphone image. To capture these images, the hand remainsstationary and the smartphone is moved in a “waving” motion across thehand. The equivalent of rolled fingerprints can be achieved by usingtechniques that create 3-dimensional images from multiple 2-dimensionalimages.

The essence of an image is a projection from a 3-dimensional view onto a2-dimensional plane, during which process the depth is lost. The3-dimensional point corresponding to a specific image point isconstrained to be on the line of sight. From a single image, it isimpossible to determine which point on this line corresponds to theimage point. If two images are available, then the position of a3-dimensional point can be found as the intersection of the twoprojection rays. This process is referred to as triangulation. A key forthis process is the relations between multiple views that convey theinformation that corresponding sets of points must contain somestructure and that this structure is related to the poses andcharacteristics of the camera. FIG. 11 shows multiple poses of a handthat can be used to create 3-dimensional fingerprint images. In FIG. 11,each of the four subimages shows a different pose of the hand. Forexample, the two subimages on the left show two poses where the pinkyfinger is in front of other fingers, whereas the two subimages on theright show two other poses where the pinky finger is all the way in theback.

Even with image improvements, there will still be cases where imagequality is poor due to a variety of factors including but not limited topoor lighting, lack of focus, movement of the subject or occlusion. Ifany of these factors occurs in a photograph, there is not sufficientinformation in the single image to overcome the problem.

One technique for improving image quality is to capture more than onimage. Smartphones are capable of capturing multiple images insuccession as a “burst” of photographs or alternatively capture seriesof images through video. These bursts can help improve quality forseveral reasons including but not limited to the following.

First, using super-resolution techniques, multiple pictures of the samefinger can be composited into a single image of improved quality.Super-resolution is a technique for extracting information frommultiple, but slightly different images of the same subject. Given theslight differences in the images, it becomes possible to inferresolution at a level finer than the resolution of the individualimages. Super-resolution is a well understood and documented techniquethat is herein presented by reference. FIG. 12 shows an example of ridgestructure in composite image 1204 rendered from a burst of images 1202a-d using super-resolution techniques.

Second, several images from the same finger provide improved “coverage”to resolve areas where detail might be lost due to reflection orocclusion. In the case of mobile devices, the best finger image istypically the finger directly in front of the camera lens. If multipleimages are captured rather than a single image, the images can becomposited into a single image where each finger represents its bestpicture. For this technique to work best, the mobile device should bemoved during the image capture process to obtain shots of each fingerdirectly in front of the camera.

Multiple images captured while moving the camera across the hand willensure that all fingers are captured while positioned in front of thecamera lens to eliminate lens-related distortion. Thus, it is possibleto capture several fingers in less time than conventional scanningcaptures a single finger.

One method for handling multiple images is to employ a “triptych”methodology. Historically, a triptych is an artistic work consisting ofmultiple panels (typically 3). In the present invention, the triptychconcept involves capturing multiple pictures (typically 3) by moving thetarget to a new location so a different finger is directly in front ofthe camera. This technique will capture the index, middle and ringfingers in succession through 3 separate photographs. A fourth can beadded for the little finger but it is likely the image of the littlefinger captured in conjunction with the ring finger will be of goodquality negating the need for a separate photograph of the littlefinger. In terms of user interaction, the triptych can be employed asthree separate “stops” for the screen-based target where images arecaptured at each stop. Or, the target can move on the screen and theuser simply follows the hand in the target. Pictures are capturedautomatically as the fingers pass in front of the camera as establishedby the position of the target.

In the case of the triptych method, no other processing-such asstitching or compositing—is applied to the image. The image positionedin front of the camera is the one that is chosen and used.

The heart of the afterburning approach noted above is the Ridge-SpecificMarker (“RSM”) Algorithm, which is a graph-based method for capturingcurve detail and relationships to describe objects that can bearticulated as line forms. In the case of fingerprints, latent printscan be mapped to corresponding reference prints by matching thecorresponding curvatures and locations within the friction ridges formultiple groupings.

FIG. 13 shows an overview of the “ridge-centric” matching process whenapplied to latent fingerprint matching. The top row 1302 in this figureillustrates the latent print and the bottom row 1304 shows thecorresponding relationship within the reference print. The first column1306 illustrates the construction of “seeds” in the form of Beziercurves that match in latent and reference space. The second column 1308illustrates the creation of the “warp” that captures the transformationof ridge structure from latent space to reference space due to theelasticity of skin. The third column 1310 shows the result, which is adirect mapping of the latent into reference space.

This recognition method deploys a unique method that establishes howwell one fingerprint will overlay over another. The overlay can becombined with a score that provides a quantitative assessment of the fitbetween prints with the objective of determining whether twofingerprints came from the same finger. This method is important whendealing with fingerprint photographs from smartphones since there aremany factors, e.g., focus, movement, and image occlusion due tolighting, which can cause difficulty during the matching.

Since the RSM-based method does not rely on minutiae, it is very usefulwhen fingerprint data are sparse. One such application takes the form ofan “Afterburner” where a match is made using a minutiae matcherreturning a set of ten reference prints for the matched subject. TheAfterburner is then applied to this returned set to ensure every fingerreturned, e.g., by the AFIS matches its respective mate captured by adevice configured as described herein.

FIG. 14 illustrates the process of applying and Afterburner applied toimages captured and returned from an AFIS search in accordance with oneembodiment. In step 1402, a device, such as a mobile device, captures aset of images of fingers, renders the images to fingerprints and sendsthem to an AFIS. In step 1404, the AFIS returns matching fingerprintsbase don successful matching of the rendered fingerprints. In step 1406,the afterburner process can be used to compare fingerprints returnedfrom the AFIS that did not match the rendered fingerprints.

FIG. 15 shows the end-to-end process for converting, e.g., a smartphoneimage to a viable fingerprint. The table within the figure outlines thesteps for generating the fingerprint image. Moreover, the upper rightimage in FIG. 15 shows a smartphone image of the fingers withoutapplying the fingerprint generating process, whereas the lower rightimage in FIG. 15 shows fingerprint outputs after applying thefingerprint generating process on the upper right image. It should benoted the target output is a “slap” image of 3 or 4 fingers since afingerprint slap is the most common form used for searching. Also,thumbs can be captured with an additional photograph (two thumbs placedtogether in a single photograph). Once a photograph is taken, the stepsto develop it into a fingerprint involve:

-   -   (1) Locating hands in a smartphone image (step 1502).    -   (2) Isolating fingerprint area (step 1504).    -   (3) Separating ridges and furrows through contrasting (step        1506).    -   (4) Generating a high contrast image separating ridges and        furrows (step 1508).    -   (5) If multiple photographs are taken, find corresponding        reference points that can be used to link the photographs        together (step 1510).    -   (6) Weaving multiple images into a composite view (optional)        (step 1512).    -   (7) Compression of images using WSQ or JPEG2K.    -   (8) Location of minutiae on the high contrast image.    -   (9) Generation of an AFIS query file. This file will be        ANSI/NIST-ITL 1-2011 (AN2k11) and/or EBTS 9.4 compliant for        compatibility with other biometric information systems.    -   (10) Submission to an AFIS.

FIG. 16 shows a fingerprint image 1602 compared against a rolled (livescan) reference print 1604 for the same finger. The matching wasperformed using the Ridge-Specific Marker algorithm described above.Notable is the transformation of the 3 dimensional fingerprint to fitonto the 2 dimensional reference image. This transformation provides anexcellent illustration of what physically happens when an image capturedwith a camera is mapped against a scanned image. To confirm the qualityof the match, corresponding minutiae points are shown in both images.

FIG. 17 shows the same process applied to a section of palmprint. Theinsert shows the relative size of the photographed palm 1702 to thereference palm 1704. Similar to fingerprints, minutiae points are shownon the palm image to indicate points of similarity with the photographof the actual palm.

Because they can be found “everywhere”, smartphones offer the potentialfor greatly expanding the ability of law enforcement and the military toobtain fingerprints when needed with no special equipment. The camerastypically used in smartphones offer excellent resolution and certainlyhave the power to produce high quality photographs. However, there arenumerous unique technical issues that arise when capturing fingerprintsthrough smartphones. The systems and methods described herein identifythese issues and formulated working solutions. These solutions includesoftware onboard the mobile device to control the focus of the cameraand permit the capture of metadata. Also included is image processingfunctionality that can either be in the device or cloud based to renderthe photograph or image into a true fingerprint, capture minutiae andgenerate an AFIS query. Finally, MEBA offers “AFIS Afterburner”technology to resolve images of poorer quality, if necessary.

FIG. 18 shows a schematic of two potential operational scenarios forperforming AFIS queries using, e.g., smartphone devices. In “Option 1”,(1) the smartphone captures an image of the hand; (2) the image ischecked on the device to ensure proper quality, positioning and focus;(3) the images is transferred via an Internet connection to a web-based(or cloud-based) processing service; (4) the processing service convertsthe photograph to a high contrast image and extracts the minutiae; (5)the minutiae and image are used to create a “search query” in the formatrequired by the AFIS; (6) the processing service then submits the queryto the AFIS; (7) the AFIS matches the query against a database ofreference prints; (8) the AFIS transmits the results back to theprocessing service; (9) the processing service reviews the results anddisambiguates results, if necessary; (10) the processing servicetransmits the results to the smartphone; and (11) the smartphonedisplays the results for the user.

In “Option 2”, (1) the smartphone captures an image of the hand; (2) theimage is checked on the device to ensure proper quality, positioning andfocus; (3) the image is then processed on the device; (4) the deviceconverts the photograph to a high contrast image and extracts theminutiae; (5) the minutiae and image are used to create a “search query”in the format required by the AFIS; (6) the device then submits thequery to the AFIS; (7) the AFIS matches the query against a database ofreference prints; (8) the AFIS transmits the results back to the device;(9) the device reviews the results and disambiguates results, ifnecessary; (10) device then displays the results for the user.

The distinction between the two options above is that in the firstoption, there is an intermediary web-based service that processes theimage into a search query, while the second option contains thisfunctionality on the actual smartphone device.

During the time the image is captured, the smartphone can also capturerelevant metadata. The metadata can be captured through conventionaldata entry fields and can include: name, address, age, etc. Most statedrivers' licenses have this biographic information either printed on thelicense or encoded as a barcode. During the time an image is captured, adriver's license can be concurrently photographed and the informationeither read from the license using optical character recognition ordecoded from the barcode typically on the back of the license. FIG. 19shows a photograph of fingers 1902 and a driver's license 1904 in thesame frame. The image can be automatically processed to extract thefingerprint information as well as the biographic information from thedriver's license. The metadata can ultimately be added to thefingerprint record generated by the transaction that follows thecapturing of the image.

FIG. 20 shows an example smartphone application in use. The simple userinterface provides an outline of three fingers. The user places theindex, middle and ring fingers within this template (outline) and snapsa photograph. From this information, the application determines thefingers orientation and generates an image that becomes the basis for anAFIS query.

The invention herein discussed can be extendible into wearable devicessuch as body cameras and glasses. FIG. 21 shows a set of commercialglasses 2102 that contain display, camera, processing and connectivityfunctions similar to a smartphone. These devices can be fitted with thetechnology to function in the same way as a smartphone. The user canmake menu selections from the display (in the lenses) through eyemovement. Camera control can be accomplished in the same way. Images canbe captured and processed and results presented on the displayperforming all activities in a hands free manner. An implementation ofthe present invention within a wearable appliance is shown in FIG. 13.For example, a police officer would ask a suspect to raise his/her handswhile the lens display provided an area where the hands would belocated, similar to the three finger target in the smartphone version.When the hands were properly placed to obtain an image of correctresolution, either the officer could trigger a photograph or the systemcould automatically capture the image.

FIG. 22 shows a schematic of glasses configured to perform thefunctionality described above. The device 2202 would consist ofconventional “smart glasses” equipped with heads-up video display 2206to present images to the wearer. The glasses are fitted with an outboardvideo camera 2204 (4 k video is ideal). The glasses also have a built incamera 2210 with a wide angle lens. The built in camera 2210 can beconfigured to capture the same field of view the wearer of the glassessees. The video output from the onboard and outboard cameras istransmitted to a computer, which will locate the outboard camera imagewithin the inboard camera image. This will permit the wearer of theglasses to see where the outboard camera is pointing.

The heads up display 2206 in the glasses has a view are with a targetsimilar to the one used in the mobile application. The alignment betweencameras will be such the target covers the area of view for the outboardcamera. The user will use this target to approximate distance to thehands of persons from whom fingerprints are to be extracted. The userwill position the hand of a person of interest in this target. Video ofthe target area will be captured and using methods herein discussed,focus on the fingers in the view area will be established. Focus anddistance can be established by moving the lens on the outboard camerauntil proper focus is achieved. Also, triangulation between the twocameras will guide the user's movement to establish focus and distanceas an alternative focusing method.

It will be apparent that other biometric measures can be used togenerate forms of impression evidence both on the smartphone as well as“pads” and wearable appliances such as glasses. Other biometricsinclude: face, voice and handwriting. Other forms of impression evidenceinclude latent fingerprints; toolmarks; shoe prints; and scars, marksand tattoos.

What is claimed:
 1. A system for transforming an image of a fingerprint,comprising: a mobile device, comprising: a first communicationinterface, a camera configured to capture at least one image of at leastone fingerprint, and a mobile device processor configured to executeinstructions, the instructions configured to cause the mobile deviceprocessor to receive the image from the camera and transmit them to animage processing system via the first communication interface; and animage processing system, comprising: a second communication interfaceconfigured to receive the image, and an image processor configured toexecute instructions, the instructions configured to cause the imageprocessor receive the image form the second communication interface,and: render the image into a high contrast image, establish focus andimage resolution for the image, perform noise reduction on the image,and perform distortion elimination on the image.
 2. The system of claim1, wherein rendering the image into a high contrast image comprisesapplying adaptive histogram equalization to separate between ridges andvalleys in fingerprint in the image.
 3. The system of claim 1, whereinmultiple images at multiple angles of the at least one fingerprint areobtained, and wherein the instructions are further configured to causethe image processor to fuse the multiple images and create a depth mapof the fingerprint.
 4. The system of claim 1, wherein rendering theimage into a high contrast image comprises: bandpass filtering theimage; analyzing an orientation pattern associated with the fingerprintint eh image; applying a segmentation mask to the image; smoothing anorientation field; enhancing ridges of the fingerprint in the image; andapplying quadrature to the mage.
 5. The system of claim 1, wherein theinstructions and configured to cause the mobile device processor tocontrol the camera such that an image is captured at an initial focusdistance, and then to convolved the captured image with Laplacian ofGaussian kernel to create a filtered image reflecting the amount to fineedge resolution, assign scores to the filtered image, and then cause thecamera to update the focus distance until an optimal distance isdetermined.
 6. The system of claim 1, wherein establishing imageresolution comprises determining the resolution of the image using(W*F_(L))/(S_(x)*F_(D)) where W is the width of the camera image, F_(L)is the focus length of the camera and S is the physical sensor size. 7.The system of claim 1, wherein noise reduction comprises: copy a regionof interested within the fingerprint in the image into a buffer;applying windowing function to the image; applying a Fourier transformto the image to create an FFT image; rotating each quadrant of the imageto relocate a DC component and low frequency components form the cornerto the image center; set the value of each low frequency component whoseorientation exceeds a threshold angle form a predicted orientation tozero; rotate the quadrants back to their original positions using aninverse Fourier transform to create an inverse FFT image; and replacethe region of interest in the image with the weighted sum of theoriginal image and the inverse FFT image.
 8. The system of claim 1,wherein the instructions are further configured to cause the imageprocessor to transmit the image to an Automated FingerprintIdentification System via the second communication interface.